The goal of the project is to design new or adapt existing parallel structures and algorithms for multi-core general purpose graphic processing units. Among results we expect significant acceleration of existing solutions and enlargement of processed volumes of data. Scientific hypothesis proposes that this goal may be achieved with popular GPU devices and personal computers. The project will be focused on highly attractive algorithms devoted to data intensive applications. Conducted literature studies and analysis of many implemented algorithms for GPUs showed a need of creating universal and consistent design of structures and algorithms for graphical devices including current trends in processors’ evolution. In the project we shall analyze solutions for MOLAP and time series databases. Results evaluation and load testing which will be performed with real-life data will allow to fine tune algorithms and structures to graphical devices requirements.
Because of the nature of the proposed research, the work method envisages performing the following tasks: design of theoretical foundations of a parallel-enabled algorithm for vector processor architecture; design of a prototype library focused on particular GPU processor limitations; implementation of a working prototype and efficiency measurements; prototype optimization in order to achieve maximum efficiency; publishing of results. All the research will be performed with the equipment available at the Faculty of Mathematics and Information Science WUT, using PC workstations or professional computational nodes in a server-room. The results will be statistically analyzed according to the following factors: scalability of an algorithm or a data structure when the data volume changes, importance of acceleration according to a similar CPU based solution, effective time, memory, memory transfer and parallel core complexity.
The need for computations, and data volumes to be processed, grows rapidly, even processors with streaming capabilities and parallel cores are often unable to run calculations with the necessary efficiency. Supercomputers which can be found in computational centres are both too exotic and expensive to become common. The answer to these issues are computational cards built upon multicore graphics cards which outperforms classical CPU processors. What is missing are new representations of data structures and methods of processing them, which will enable efficient programming of algorithms working within the specific limitations of a GPU. By developing new data structures and new operations utilizing all available cores the project will open up new possibilities of using graphics cards in many fields of science, where drastic efficiency improvement of data processing is crucial. The expected impact of the project will enable popular personal computers in analysis.